Concise Biostatistical Principles And Concepts
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Concise Biostatistical Principles And Concepts
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-18
Concise Biostatistical Principles And Concepts written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Medical categories.
Concise Biostatistical Principles and Concepts, 2nd Edition Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis,
Concise Biostatistical Principles And Concepts 2nd Edition
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-18
Concise Biostatistical Principles And Concepts 2nd Edition written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Medical categories.
Concise Biostatistical Principles and Concepts - Statistical Reality in Evidence Discovery Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis
Concise Biostatistical Principles And Concepts
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-17
Concise Biostatistical Principles And Concepts written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-17 with Medical categories.
Concise Biostatistical Principles and Concepts, 2nd Edition Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. This text, Concise Biostatistics for Clinical, Biomedical and Population-Based Researchers has filled this gap, not only in the way complex modeling is explained but the simplification of statistical techniques in a way that had never been explained before. This text has been prepared intentionally at the rudimentary level to benefit clinicians without sophisticated mathematical backgrounds or previous advanced knowledge of biostatics as applied statistics in health and medicine. Also, biomedical researchers who may want to conduct clinical research, as well as consumers of research products may benefit from the sampling techniques, their relevance to scientific evidence discovery as well a simplified approach to statistical modeling of clinical and biomedical research data. It is with this expectation and enthusiasm that we recommend this text to clinicians in all fields of clinical and biomedical research. One’s experience with biomedical research and how the findings in this arm are translated to the clinical environment signals the need for the application of biological, and clinical relevance of findings prior to statistical inference. The examples provided by the author to simplify research methods are familiar to orthopedic surgeons as well as clinicians in other specialties of medicine and surgery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis,
Modern Biostatistical Principles And Conduct
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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-14
Modern Biostatistical Principles And Conduct written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Medical categories.
Modern Biostatistical Principles & Conduct - Clinical Medicine and Public/Population Health Assessment Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. This text, Modern Biostatistics for Clinical, Biomedical and Population-Based Researchers has filled this gap, not only in the way complex modeling is explained but the simplification of statistical techniques in a way that had never been explained before. This text has been prepared intentionally at the rudimentary level to benefit clinicians without sophisticated mathematical backgrounds or previous advanced knowledge of biostatics as applied statistics in health and medicine. Also, biomedical researchers who may want to conduct clinical research, as well as consumers of research products may benefit from the sampling techniques, their relevance to scientific evidence discovery as well a simplified approach to statistical modeling of clinical and biomedical research data. It is with this expectation and enthusiasm that we recommend this text to clinicians in all fields of clinical and biomedical research. One’s experience with biomedical research and how the findings in this arm are translated to the clinical environment signals the need for the application of biological, and clinical relevance of findings prior to statistical inference. The examples provided by the author to simplify research methods are familiar to orthopedic surgeons as well as clinicians in other specialties of medicine and surgery. Whereas statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading, and meaningless. The authors have attempted to deemphasize the p-value in the interpretation of clinical and biomedical research findings, by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality, the difference is too insignificant to warrant any clinical importance. In contrast, a small study as frequently seen in most clinical trials or surgical research may have a large effect size of clinical relevance but not statistically significant at (p > 0.05). Thus, without considering the magnitude of the effect size with the confidence interval, we tend to regard these studies as negative findings, which is erroneous, since the absence of evidence, simply on the basis of an arbitrary significance level of 5% does not necessarily mean evidence of absence.1 In effect, clinical research results, cannot be adequately interpreted without first considering the biological and clinical significance of the data, before the statistical stability of the findings (p-value and 95% Confidence Interval), since the p-value as observed by the authors merely reflects the size of the study and not the measure of evidence. In recommending this text, it is one’s inclination that this book will benefit clinicians, research fellows, clinical fellows, postdoctoral students in biomedical and clinical settings, nurses, clinical research coordinators, physical therapists, and all those involved in clinical research design, conduct, and analysis of research data for statistical and clinical relevance. Convincingly, knowledge gained from this text will lead to our improvement of patient care through well-conceptualized research. Therefore, with the knowledge that no book is complete, no matter its content or volume, especially a book of this nature, which is prepared to guide clinicians on sampling, statistical modeling of data, and interpretation of findings, this book will benefit clinicians who are interested in applying appropriate statistical technique to scientific evidence discovery. Finally, we are optimistic that this book will bridge the gap in knowledge and practice of clinical and biomedical research, especially for clinicians in busy practice who are passionate about making a difference in their patient's care through scientific research initiatives.
Applied Biostatistical Principles And Concepts
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Author : Laurens Holmes, Jr.
language : en
Publisher: Routledge
Release Date : 2017-11-22
Applied Biostatistical Principles And Concepts written by Laurens Holmes, Jr. and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Medical categories.
The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations. This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of "big data" type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.
Basic Biostatistics
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Author : Gerstman
language : en
Publisher: Jones & Bartlett Publishers
Release Date : 2014-02-07
Basic Biostatistics written by Gerstman and has been published by Jones & Bartlett Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-07 with Education categories.
Basic Biostatistics is a concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. The text puts equal emphasis on exploratory and confirmatory statistical methods. Sampling, exploratory data analysis, estimation, hypothesis testing, and power and precision are covered through detailed, illustrative examples. The book is organized into three parts: Part I addresses basic concepts and techniques; Part II covers analytic techniques for quantitative response variables; and Part III covers techniques for categorical responses. The Second Edition offers many new exercises as well as an all new chapter on "Poisson Random Variables and the Analysis of Rates." With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health. Features: Illustrative, relevant examples and exercises incorporated throughout the book. Answers to odd-numbered exercises provided in the back of the book. (Instructors may requests answers to even-numbered exercises from the publisher. Chapters are intentionally brief and limited in scope to allow for flexibility in the order of coverage. Equal attention is given to manual calculations as well as the use of statistical software such as StaTable, SPSS, and WinPepi. Comprehensive Companion Website with Student and Instructor's Resources.
Principles And Applications Of Biostatistics
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Author : Ray M. Merrill
language : en
Publisher: Jones & Bartlett Learning
Release Date : 2021-09-03
Principles And Applications Of Biostatistics written by Ray M. Merrill and has been published by Jones & Bartlett Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-03 with Education categories.
Principles and Applications of Biostatistics covers the primary concepts and methods that are required for a fundamental understanding of the use and interpretation of statistics for the biological and health sciences–from data presentation to multiple regression and analysis of variance. With a focus clarity, brevity, and accuracy, this text provides understandable and focused explanation of statistical principles and applications along with practical examples (provided in R and Microsoft Excel) and problems drawn from biological health and medical settings. Key Features: • Practical questions follow each problem to encourage students to consider why the problem likely exists, help formulate hypotheses, and then statistically assess those hypotheses. • Abundant assignment problems at the end of sections and each chapter cover a variety of application areas of biostatistics. • Rationale boxes offer explanations of why certain methods are used for specific cases.
Principles Of Biostatistics
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Author : Marcello Pagano
language : en
Publisher: CRC Press
Release Date : 2018-02-19
Principles Of Biostatistics written by Marcello Pagano and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-19 with Mathematics categories.
This edition is a reprint of the second edition published in 2000 by Brooks/Cole and then Cengage Learning. Principles of Biostatistics is aimed at students in the biological and health sciences who wish to learn modern research methods. It is based on a required course offered at the Harvard School of Public Health. In addition to these graduate students, many health professionals from the Harvard medical area attend as well. The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize, explore, and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling, thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them. All supplements, including a manual for students with solutions for odd-numbered exercises, a manual for instructors with solutions to all exercises, and selected data sets, are available at http://www.crcpress.com/9781138593145.
Essential Biostatistics
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Author : Harvey Motulsky
language : en
Publisher: Oxford University Press
Release Date : 2016
Essential Biostatistics written by Harvey Motulsky and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Literary Criticism categories.
With its engaging and conversational tone, Essential Biostatistics: A Nonmathematical Approach provides a clear introduction to statistics for students in a wide range of fields, and a concise statistics refresher for scientists and professionals who need to interpret statistical results. It explains the ideas behind statistics in nonmathematical terms, offers perspectives on how to interpret published statistical results, and points out common conceptual traps to avoid. It can be used as a stand-alone text or as a supplement to a traditional statistics textbook.
Understanding And Applying Basic Statistical Methods Using R
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Author : Rand R. Wilcox
language : en
Publisher: John Wiley & Sons
Release Date : 2016-06-07
Understanding And Applying Basic Statistical Methods Using R written by Rand R. Wilcox and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-07 with Social Science categories.
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox’ Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.